Papers by Guangnan Ye
GQLBench: A Large-Scale Cross-Domain, Cross-Dialect Benchmark for NL2GQL (2026.acl-long)
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| Challenge: | despite growing interest in NL2GQL, benchmarking progress has been constrained by the lack of resources that are simultaneously large-scale, cross-domain, and cross-dialect. |
| Approach: | They propose a framework that integrates NL2SQL-to-NL2GQL conversion with graph-native data generation. |
| Outcome: | The proposed framework supports execution-based evaluation on Cypher and ISO-GQL, covering hundreds of graph databases and over 20k natural language questions for each dialect. |
PRISM: Probabilistic Reward Model with Inherent Structural Modeling (2026.acl-long)
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| Challenge: | Existing evaluators compress diverse human judgments into a single scalar, leading to brittle alignment and reward hacking. |
| Approach: | They propose a Gaussian-based reinterpretation of reward evaluation as a conditional distribution and a mixture of Gaussians to capture conflicting preference dimensions. |
| Outcome: | The proposed model outperforms scalar baselines in accuracy and generalization. |
On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation (2021.acl-long)
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| Challenge: | Existing methods for explaining "black-box" models such as Influence Functions are becoming more popular. |
| Approach: | They propose a semantic-based evaluation metric that can better align with humans’ judgment of explanations than the widely adopted diagnostic or re-training measures. |
| Outcome: | The proposed method can better align with humans’ judgment of explanations than diagnostic or re-training measures. |
Rethinking the Role of Entropy in Optimizing Tool-Use Behaviors for Large Language Model Agents (2026.acl-long)
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Zeping Li, Hongru Wang, Yiwen Zhao, Guanhua Chen, Yixia Li, Keyang Chen, Yixin Cao, Guangnan Ye, Hongfeng Chai, Zhenfei Yin
| Challenge: | Large language models excel in mathematical reasoning and multi-hop question answering tasks, but in long trajectories, agents often invoke tools excessively or inappropriately, increasing computation cost and derailing the reasoning process. |
| Approach: | They propose to use entropy reduction as a supervisory signal to reduce tool calls . they propose to design two reward strategies to address the needs of optimizing tool-use behavior. |
| Outcome: | The proposed reward strategies reduce tool calls by 72.07% and improve performance by 22.27%. |
Behavioral Consistency Validation for LLM Agents: An Analysis of Trading-Style Switching through Stock-Market Simulation (2026.findings-acl)
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Zeping Li, Guancheng Wan, Keyang Chen, Yu Chen, Yiwen Zhao, Philip Torr, Guangnan Ye, Zhenfei Yin, Hongfeng Chai
| Challenge: | Recent studies have applied Large Language Models (LLMs) as agents in financial stock market simulations to test if micro-level behaviors aggregate into macro-level phenomena. |
| Approach: | They propose four alignment metrics and use Mann–Whitney U tests to compare agents’ style-switching behavior with financial theory. |
| Outcome: | The proposed model is only partially consistent with financial theory. |
Are LLMs Rational Investors? A Study on the Financial Bias in LLMs (2025.findings-acl)
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Yuhang Zhou, Yuchen Ni, Zhiheng Xi, Zhangyue Yin, Yu He, Gan Yunhui, Xiang Liu, Zhang Jian, Sen Liu, Xipeng Qiu, Yixin Cao, Guangnan Ye, Hongfeng Chai
| Challenge: | Existing studies on biases within specific domains, such as finance, remain limited. |
| Approach: | They propose a framework to detect, detect, analyze and mitigate financial biases in large language models. |
| Outcome: | The proposed framework reduces bias by 68% for the most biased model, according to key metrics. |
R3-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL (2024.findings-emnlp)
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Yuhang Zhou, Yu He, Siyu Tian, Yuchen Ni, Zhangyue Yin, Xiang Liu, Chuanjun Ji, Sen Liu, Xipeng Qiu, Guangnan Ye, Hongfeng Chai
| Challenge: | Adapting existing approaches for converting natural language to SQL encounters hurdles due to distinct nature of GQL compared to SQL. |
| Approach: | They propose a method that integrates both small and large Foundation Models for ranking, rewriting, and refining tasks. |
| Outcome: | The proposed approach integrates both small and large Foundation Models for ranking, rewriting, and refining tasks while capitalizing on the superior generalization and query generation prowess of larger models for the final transformation of natural language queries into GQL formats. |